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Matteo Corno jointly received the Master of Science in Computer and Electrical Engineering from the University of Illinois at Chicago and the “Laurea” Degree cum laude from the Politecnico di Milano in 2005. In 2006, he joined the Ph.D. program in Control Engineering at Politecnico di Milano where he graduated in 2009 cum laude. From 2009 to 2011 he was Assistant Professor at the Delft University of Technology. In 2011 he joined the Politecnico di Milano as an Assistant professor. Since 2015, he is an associate professor in the same university. He held several visiting positions among others at: Johannes Kepler University in Linz, University of Minnesota and Thales Alenia Space. During his career he won 2 Honda Initiation Grants, the IFAC Congress Interactive Paper Prize at the 19th IFAC World Congress in Cape Town and an award for the best paper published in Control Engineering Practice and the Guglielmo Marconi Prize for Industrial Innovation by the Accademia Nazionale dei Lincei. Starting from 2015, he is the Principal Investigator of the Scientific Independence of young Researchers project: Online Accurate Battery State Estimation via Electrochemical Modelling His research interests include automotive control systems, nonlinear estimation techniques applied to Battery Management Systems and haptic interfaces.
Semi-active control is the most employed technology for electronic suspension systems. The damping can be regulated to trade-off comfort and handling. Due to its success in industrial applications, semi-active control design has been extensively investigated in literature mainly from a model-based perspective. In this contribution, the authors propose a novel control strategy derived via a sequential learning framework, which selects the most significant feedback measurements for semi-active control and learns the optimal policy from data. As opposed to most of the contributions based on deep-learning approaches, the output of the proposed methodology is a control algorithm consisting of few parameters, which can be easily ported and calibrated on a real vehicle. Experimental validation on a sports-car shows that the proposed algorithm is superior in damping the body resonance with respect to the state-of-the-art skyhook algorithm. Indeed, the learned control policy consists of an augmentation of skyhook.
Gianluca Savaia; Simone Formentin; Giulio Panzani; Matteo Corno; Sergio M. Savaresi. Enhancing skyhook for semi-active suspension control via machine learning. IFAC Journal of Systems and Control 2021, 17, 100161 .
AMA StyleGianluca Savaia, Simone Formentin, Giulio Panzani, Matteo Corno, Sergio M. Savaresi. Enhancing skyhook for semi-active suspension control via machine learning. IFAC Journal of Systems and Control. 2021; 17 ():100161.
Chicago/Turabian StyleGianluca Savaia; Simone Formentin; Giulio Panzani; Matteo Corno; Sergio M. Savaresi. 2021. "Enhancing skyhook for semi-active suspension control via machine learning." IFAC Journal of Systems and Control 17, no. : 100161.
Magneto-rheological dampers are an effective technology to control the damping coefficient of a semi-active suspension. Most of the contributions in literature propose damper models to be used in simulation, or as damping force virtual sensors in control applications. Typically, phenomenological models or complex black-box approaches, relying on Neural Networks, are employed. In this work, we propose a semi-active MR model based on a Hammerstein–Wiener scheme, meant not only for force estimation but also – in a more genuinely control-oriented perspective – to be proactively used in the suspension controller design. Despite being a black-box model, each component is shown to serve for the characterization of a specific feature of the MR damper, and its identification is done thanks to an ad hoc design of experiments. In particular, the Wiener part of the model is shown to be essential for the proper modelling of the magnetization dynamics of the magneto-rheological fluid, which usually is a neglected aspect in control-oriented models. The proposed scheme is validated on a testbench using realistic road solicitations.
Gianluca Savaia; Giulio Panzani; Matteo Corno; Jacopo Cecconi; Sergio M. Savaresi. Hammerstein–Wiener modelling of a magneto-rheological dampers considering the magnetization dynamics. Control Engineering Practice 2021, 112, 104829 .
AMA StyleGianluca Savaia, Giulio Panzani, Matteo Corno, Jacopo Cecconi, Sergio M. Savaresi. Hammerstein–Wiener modelling of a magneto-rheological dampers considering the magnetization dynamics. Control Engineering Practice. 2021; 112 ():104829.
Chicago/Turabian StyleGianluca Savaia; Giulio Panzani; Matteo Corno; Jacopo Cecconi; Sergio M. Savaresi. 2021. "Hammerstein–Wiener modelling of a magneto-rheological dampers considering the magnetization dynamics." Control Engineering Practice 112, no. : 104829.
The End-of-Line (EoL) calibration of semi-active suspension systems for road vehicles is usually a critical and expensive task, needing a team of vehicle and control experts as well as many hours of professional driving. In this paper, we propose a purely data-based tuning method enabling the automatic calibration of the parameters of a proprietary suspension controller by relying on little experimental time and exploiting Bayesian Optimization tools. A detailed methodology on how to select the most critical degrees of freedom of the algorithm is also provided. The effectiveness of the proposed approach is assessed on a commercial multi-body simulator as well as on a real car.
Gianluca Savaia; Youngil Sohn; Simone Formentin; Giulio Panzani; Matteo Corno; Sergio M. Savaresi. Experimental automatic calibration of a semi-active suspension controller via Bayesian Optimization. Control Engineering Practice 2021, 112, 104826 .
AMA StyleGianluca Savaia, Youngil Sohn, Simone Formentin, Giulio Panzani, Matteo Corno, Sergio M. Savaresi. Experimental automatic calibration of a semi-active suspension controller via Bayesian Optimization. Control Engineering Practice. 2021; 112 ():104826.
Chicago/Turabian StyleGianluca Savaia; Youngil Sohn; Simone Formentin; Giulio Panzani; Matteo Corno; Sergio M. Savaresi. 2021. "Experimental automatic calibration of a semi-active suspension controller via Bayesian Optimization." Control Engineering Practice 112, no. : 104826.
This article proposes an energy management system (EMS) for shared electric bicycles. The objective is to guarantee electric assistance to the cyclist while avoiding discharging the battery. The basic working principle exploits the cycling efficiency gaps. The proposed multilayered EMS is specifically tailored to a free-floating bike-sharing setting. The innermost layer manages the assistance and energy harvesting with the objective of yielding an intuitive human-machine interface. The middle level modulates the level of assistance so to track a desired average battery power. This is an adaptive model-based controller designed on a control-oriented model of the cyclist and bicycle energy dynamics. A cyclist profiling mechanism enables the model adaptation. The outermost loop guarantees the long-term robustness by tracking a desired battery state-of-charge profile. Extensive simulations and experimental tests validate this approach in terms of usability and charge sustenance, proving that the cyclist profiling is of paramount importance.
Matteo Corno; Alessandra Duz; Sergio M. Savaresi. Design of a Charge-Sustaining Energy Management System for a Free-Floating Electric Shared Bicycle. IEEE Transactions on Control Systems Technology 2021, PP, 1 -13.
AMA StyleMatteo Corno, Alessandra Duz, Sergio M. Savaresi. Design of a Charge-Sustaining Energy Management System for a Free-Floating Electric Shared Bicycle. IEEE Transactions on Control Systems Technology. 2021; PP (99):1-13.
Chicago/Turabian StyleMatteo Corno; Alessandra Duz; Sergio M. Savaresi. 2021. "Design of a Charge-Sustaining Energy Management System for a Free-Floating Electric Shared Bicycle." IEEE Transactions on Control Systems Technology PP, no. 99: 1-13.
Autonomous driving is greatly impacting intensive and precise agriculture. Matter-of-factly, the first commercial applications of autonomous driving were in autonomous navigation of agricultural tractors in open fields. As the technology improves, the possibility of using autonomous or semi-autonomous tractors in orchards and vineyards is becoming commercially profitable. These scenarios offer more challenges as the vehicle needs to position itself with respect to a more cluttered environment. This paper presents an adaptive localization system for (semi-) autonomous navigation of agricultural tractors in vineyards that is based on ultrasonic automotive sensors. The system estimates the distance from the left vineyard row and the incidence angle. The paper shows that a single tuning of the localization algorithm does not provide robust performance in all vegetation scenarios. We solve this issue by implementing an Extended Kalman Filter (EKF) and by introducing an adaptive data selection stage that automatically adapts to the vegetation conditions and discards invalid measurements. An extensive experimental campaign validates the main features of the localization algorithm. In particular, we show that the Root Mean Square Error (RMSE) of the distance is 16 cm, while the angular RMSE is 2.6 degrees.
Matteo Corno; Sara Furioli; Paolo Cesana; Sergio Savaresi. Adaptive Ultrasound-Based Tractor Localization for Semi-Autonomous Vineyard Operations. Agronomy 2021, 11, 287 .
AMA StyleMatteo Corno, Sara Furioli, Paolo Cesana, Sergio Savaresi. Adaptive Ultrasound-Based Tractor Localization for Semi-Autonomous Vineyard Operations. Agronomy. 2021; 11 (2):287.
Chicago/Turabian StyleMatteo Corno; Sara Furioli; Paolo Cesana; Sergio Savaresi. 2021. "Adaptive Ultrasound-Based Tractor Localization for Semi-Autonomous Vineyard Operations." Agronomy 11, no. 2: 287.
This paper studies the application of in-wheel suspensions and airless tires to bicycles. The paper analyzes the in-wheel suspension solution in two interwoven directions: a model-based study and an experimental analysis. On the one hand, a multi-body simulator allows for detailed dynamic and design sensitivities analyses. On the other hand, the proposed architecture is experimentally compared to a classical bicycle with pneumatic tires. The analysis, mainly focusing on the in-plane dynamics, shows that the suspension system improves comfort (road filtering) of more than 20% on all tested road surfaces (with the exception of cobblestone), but at the same time reduces the vehicle efficiency of about 14%. The loss of efficiency is due to the periodic compression and extension of the rotating suspension assembly.
Matteo Corno; Giulio Panzani; Edoardo Catenaro; Sergio M. Savaresi. Modeling and analysis of a bicycle equipped with in-wheel suspensions. Mechanical Systems and Signal Processing 2021, 155, 107548 .
AMA StyleMatteo Corno, Giulio Panzani, Edoardo Catenaro, Sergio M. Savaresi. Modeling and analysis of a bicycle equipped with in-wheel suspensions. Mechanical Systems and Signal Processing. 2021; 155 ():107548.
Chicago/Turabian StyleMatteo Corno; Giulio Panzani; Edoardo Catenaro; Sergio M. Savaresi. 2021. "Modeling and analysis of a bicycle equipped with in-wheel suspensions." Mechanical Systems and Signal Processing 155, no. : 107548.
Safe and effective exploitation of Lithium Ion batteries requires advanced battery management systems (BMS). This paper proposes a computationally efficient, control-oriented model of a Li-ion cell. The model describes the spatial nature of both the chemical species and temperature dynamics in a computationally efficient way. The method takes advantage of the algebraic structure that arises from the distributed nature of the model. We show that, by discretizing the model partial differential equations with a finite difference method, the coupling equations take a semi separable structure for which an efficient algebra exists. This approach yields an efficient modeling tool that can be employed to design model-based estimation and control algorithms. The proposed model is validated against a high order computational fluid dynamics (CFD) model showing accuracy and efficiency.
Matteo Corno. Efficient Control-Oriented Coupled Electrochemical Thermal Modeling of Li-Ion Cells. IEEE Transactions on Industrial Electronics 2020, 68, 7024 -7033.
AMA StyleMatteo Corno. Efficient Control-Oriented Coupled Electrochemical Thermal Modeling of Li-Ion Cells. IEEE Transactions on Industrial Electronics. 2020; 68 (8):7024-7033.
Chicago/Turabian StyleMatteo Corno. 2020. "Efficient Control-Oriented Coupled Electrochemical Thermal Modeling of Li-Ion Cells." IEEE Transactions on Industrial Electronics 68, no. 8: 7024-7033.
This article deals with trajectory tracking for autonomous cars during evasive maneuvers and in the presence of steering actuator nonlinearities. This article develops an LPV MISO H-infinity controller based on the feedback of the lateral error at the center of gravity and the look-ahead distance. The controller architecture offers a way to cope with the effect of the steering nonlinearities, by scheduling one of the control weighting functions. A detailed experimental validation on three different maneuvers (straight driving, wide bend, and a double-lane change) shows the effectiveness of the proposed LPV solution.
Matteo Corno; Giulio Panzani; Federico Roselli; Michele Giorelli; Davide Azzolini; Sergio M. Savaresi. An LPV Approach to Autonomous Vehicle Path Tracking in the Presence of Steering Actuation Nonlinearities. IEEE Transactions on Control Systems Technology 2020, 29, 1766 -1774.
AMA StyleMatteo Corno, Giulio Panzani, Federico Roselli, Michele Giorelli, Davide Azzolini, Sergio M. Savaresi. An LPV Approach to Autonomous Vehicle Path Tracking in the Presence of Steering Actuation Nonlinearities. IEEE Transactions on Control Systems Technology. 2020; 29 (4):1766-1774.
Chicago/Turabian StyleMatteo Corno; Giulio Panzani; Federico Roselli; Michele Giorelli; Davide Azzolini; Sergio M. Savaresi. 2020. "An LPV Approach to Autonomous Vehicle Path Tracking in the Presence of Steering Actuation Nonlinearities." IEEE Transactions on Control Systems Technology 29, no. 4: 1766-1774.
This brief proposes an electrochemical model-based estimator of the Lithium-ion (Li-ion) concentration and temperature of a Li-ion cell. The use of the electrochemical approach allows for the estimation of the spatial distribution of lithium concentration and temperature. The estimation is based on a soft-constrained dual unscented Kalman filter (DUKF) designed on the pseudo-2-D model of a Li-ion cell. The dual structure, along with parallelization, reduces the computational complexity, whereas the soft-constraint improves convergence. A simulation analysis validates the approach showing bulk state of charge (SoC) estimation error lower than 1.5%, solid-phase lithium concentration estimation errors of less than 4%, and temperature estimation errors within 0.2 °C from the true value in any point of the cell.
Stefano Marelli; Matteo Corno. Model-Based Estimation of Lithium Concentrations and Temperature in Batteries Using Soft-Constrained Dual Unscented Kalman Filtering. IEEE Transactions on Control Systems Technology 2020, 1 -8.
AMA StyleStefano Marelli, Matteo Corno. Model-Based Estimation of Lithium Concentrations and Temperature in Batteries Using Soft-Constrained Dual Unscented Kalman Filtering. IEEE Transactions on Control Systems Technology. 2020; (99):1-8.
Chicago/Turabian StyleStefano Marelli; Matteo Corno. 2020. "Model-Based Estimation of Lithium Concentrations and Temperature in Batteries Using Soft-Constrained Dual Unscented Kalman Filtering." IEEE Transactions on Control Systems Technology , no. 99: 1-8.
Autonomous parcel delivery is attracting a lot of interest. Terrestrial delivery drones travel at lower speeds, are smaller and lighter than passenger cars. These features make them an ideal and valuable first step and experimental sandbox toward fully autonomous vehicles. To be useful, however, small wheeled drones need to operate on parts of the roads that are reserved to pedestrians. This is a challenge by itself. Pedestrian areas are less structured than road and abide by looser rules. The best route for a delivery drone may not be the shortest path; other aspects need to be accounted for that make a route more or less practical for the specific features of the vehicle. This paper introduces a quantitative analysis of these specific issues. The paper proposes a quantitative index that asses a route practicability for a small terrestrial drone. It combines different aspects that account for sidewalk width, sidewalk surface condition, route length and the number of driveways and crosswalks present on the way. We provide the mathematical definition of the index, and use our wheeled drone prototype to show how it can be used to classify and chose the best routes among a selection. Although the index is designed for autonomous drones, given the specific dynamic features of the drone, it can also be employed as is to quantify the accessibility of different routes for disabled people.
Matteo Corno; Sergio Savaresi. Measuring Urban Sidewalk Practicability: a Sidewalk Robot Feasibility Index. IFAC-PapersOnLine 2020, 53, 15053 -15058.
AMA StyleMatteo Corno, Sergio Savaresi. Measuring Urban Sidewalk Practicability: a Sidewalk Robot Feasibility Index. IFAC-PapersOnLine. 2020; 53 (2):15053-15058.
Chicago/Turabian StyleMatteo Corno; Sergio Savaresi. 2020. "Measuring Urban Sidewalk Practicability: a Sidewalk Robot Feasibility Index." IFAC-PapersOnLine 53, no. 2: 15053-15058.
The suspension system has an important impact on vehicle dynamics, comfort and stability; these aspects are conflicting and the objective of an automatic control is to find a compromise. In this paper, the authors present an approach for the control of the vertical dynamics consisting of two layers: a low-level controller which fully exploits the properties of the magneto-rheological damper technology, and a high-level controller based upon a linearized skyhook for the full body control. The controller is experimentally validated on an actual vehicle in two road scenarios, which differ in their frequency excitation.
Gianluca Savaia; Matteo Corno; Giulio Panzani; Andrea Sinigaglia; Sergio M. Savaresi. Experimental Validation of a Hierarchical Suspension Control via MR Damper. IFAC-PapersOnLine 2020, 53, 14401 -14406.
AMA StyleGianluca Savaia, Matteo Corno, Giulio Panzani, Andrea Sinigaglia, Sergio M. Savaresi. Experimental Validation of a Hierarchical Suspension Control via MR Damper. IFAC-PapersOnLine. 2020; 53 (2):14401-14406.
Chicago/Turabian StyleGianluca Savaia; Matteo Corno; Giulio Panzani; Andrea Sinigaglia; Sergio M. Savaresi. 2020. "Experimental Validation of a Hierarchical Suspension Control via MR Damper." IFAC-PapersOnLine 53, no. 2: 14401-14406.
Autonomous navigation on sidewalks and pedestrian areas is a complex problem, that requires the solution of different challenging tasks. One that is particularly hard to tackle is that of autonomous street crossing, which requires the robot to be aware of the position and speed of surrounding vehicles in order to decide whether is safe to cross. This work is dedicated to the development of an obstacle speed estimation algorithm to be applied to the context of autonomous navigation at crosswalks. In particular, a novel approach to the extended-target tracking problem is presented, which leverages a nested structure and a clustering algorithm that reduces the problem to a standard target tracking one. The effectiveness of the algorithm is demonstrated through testing on a prototype parcel-delivery robot operating in a real-world urban environment.
Filippo Parravicini; Matteo Corno; Sergio Savaresi. Extended target tracking for autonomous street crossing. IFAC-PapersOnLine 2020, 53, 15440 -15445.
AMA StyleFilippo Parravicini, Matteo Corno, Sergio Savaresi. Extended target tracking for autonomous street crossing. IFAC-PapersOnLine. 2020; 53 (2):15440-15445.
Chicago/Turabian StyleFilippo Parravicini; Matteo Corno; Sergio Savaresi. 2020. "Extended target tracking for autonomous street crossing." IFAC-PapersOnLine 53, no. 2: 15440-15445.
In this work, a closed-loop battery aging management strategy for electric vehicles is proposed. The aging management strategy, following the model predictive control rationale, optimizes aging and vehicle performance online. The proposed formulation is based on a closed-loop term which aims at tracking a user defined aging profile. A thorough simulation study validates the approach and verifies its robustness against model uncertainties and anomalous aging phenomena.
Gabriele Pozzato; Matteo Corno. Closed-loop Battery Aging Management for Electric Vehicles. IFAC-PapersOnLine 2020, 53, 14199 -14204.
AMA StyleGabriele Pozzato, Matteo Corno. Closed-loop Battery Aging Management for Electric Vehicles. IFAC-PapersOnLine. 2020; 53 (2):14199-14204.
Chicago/Turabian StyleGabriele Pozzato; Matteo Corno. 2020. "Closed-loop Battery Aging Management for Electric Vehicles." IFAC-PapersOnLine 53, no. 2: 14199-14204.
Solid-state LIDAR technology has recently emerged, allowing for smaller and more affordable devices. In the present work, we investigate the possibility of using a vehicle mounted solid-state LIDAR to estimate the vehicle pitch and heave dynamics. We present and compare two approaches: a model-based estimation and a data driven algorithm. The algorithms are tested on an instrumented vehicle. The experimental results show that the data-driven approach outperforms the model-based estimation in estimating pitch caused both by accelerations and braking and by road disturbances.
Donald Selmanaj; Matteo Corno; Giulio Panzani; Sergio M. Savaresi. On Vehicle Pitch Estimation via solid-state LIDAR. IFAC-PapersOnLine 2020, 53, 13904 -13909.
AMA StyleDonald Selmanaj, Matteo Corno, Giulio Panzani, Sergio M. Savaresi. On Vehicle Pitch Estimation via solid-state LIDAR. IFAC-PapersOnLine. 2020; 53 (2):13904-13909.
Chicago/Turabian StyleDonald Selmanaj; Matteo Corno; Giulio Panzani; Sergio M. Savaresi. 2020. "On Vehicle Pitch Estimation via solid-state LIDAR." IFAC-PapersOnLine 53, no. 2: 13904-13909.
This paper presents an obstacle detection system for snow groomers. The system is based on a 2D solid-states LiDAR sensor mounted on the top of the cabin. The measurements describe the surrounding environment through an Occupancy Grid framework, which is extended for this particular case study. The proposed approach set the occupancy probability of the surrounding environment based on the expected height of the obstacle. The method is extensively analyzed through experimental test on a snow groomer.
L. Onesto; M. Corno; L. Franceschetti; E. Hokka; S.M. Savaresi. LiDAR Based Obstacle detection for Snow Groomers. IFAC-PapersOnLine 2020, 53, 15469 -15474.
AMA StyleL. Onesto, M. Corno, L. Franceschetti, E. Hokka, S.M. Savaresi. LiDAR Based Obstacle detection for Snow Groomers. IFAC-PapersOnLine. 2020; 53 (2):15469-15474.
Chicago/Turabian StyleL. Onesto; M. Corno; L. Franceschetti; E. Hokka; S.M. Savaresi. 2020. "LiDAR Based Obstacle detection for Snow Groomers." IFAC-PapersOnLine 53, no. 2: 15469-15474.
The battery pack accounts for a large share of an Electric Vehicle cost. In this context, making sure that the battery pack life matches the lifetime of the vehicle is critical. The present work proposes a battery aging management framework which is capable of controlling the battery capacity degradation while guaranteeing acceptable vehicle performance in terms of driving range, recharge time, and drivability. The strategy acts on the maximum battery current, and on the depth of discharge. The formalization of the battery management issue leads to a multi-objective, multi-input optimization problem for which we propose an online solution. The algorithm, given the current battery residual capacity and a prediction of the driver's behavior, iteratively selects the best control variables over a suitable control discretization step. We show that the best aging strategy depends on the driving style. The strategy is thus made adaptive by including a self-learnt, Markov-chain-based driving style model in the optimization routine. Extensive simulations demonstrate the advantages of the proposed strategy against a trivial strategy and an offline benchmark policy over a life of 200,000 km.
Matteo Corno; Gabriele Pozzato. Active Adaptive Battery Aging Management for Electric Vehicles. IEEE Transactions on Vehicular Technology 2019, 69, 258 -269.
AMA StyleMatteo Corno, Gabriele Pozzato. Active Adaptive Battery Aging Management for Electric Vehicles. IEEE Transactions on Vehicular Technology. 2019; 69 (1):258-269.
Chicago/Turabian StyleMatteo Corno; Gabriele Pozzato. 2019. "Active Adaptive Battery Aging Management for Electric Vehicles." IEEE Transactions on Vehicular Technology 69, no. 1: 258-269.
This work proposes an analysis of the pitch dynamics of a heavy-duty vehicle, namely an agricultural tractor. Considering maneuvers performed on a flat-asphalt surface, the analysis is performed through an image processing approach. The analysis focuses on the cabin displacement and on the vehicle body displacement. Moreover, the tires compression and the vehicle longitudinal slip are evaluated. The analysis shows how the cabin and the body displacements change in function of the vehicle longitudinal acceleration and how, due to the tires compression, the cabin and the body can oscillate, at the end of a braking maneuver. The results are used to evaluate the feasibility of a road gradient estimator based on the inertial measurement of a mono axial accelerometer installed in the cabin. In particular, the cabin displacement needs to be considered and an additional sensor which measures the cabin speed is required to avoid a drop of performance.
L. Onesto; M. Corno; S. Savaresi. Pitch Dynamics Analysis for an Agricultural Tractor with Image Processing Validation through an Off-Board Camera. IFAC-PapersOnLine 2019, 52, 492 -497.
AMA StyleL. Onesto, M. Corno, S. Savaresi. Pitch Dynamics Analysis for an Agricultural Tractor with Image Processing Validation through an Off-Board Camera. IFAC-PapersOnLine. 2019; 52 (5):492-497.
Chicago/Turabian StyleL. Onesto; M. Corno; S. Savaresi. 2019. "Pitch Dynamics Analysis for an Agricultural Tractor with Image Processing Validation through an Off-Board Camera." IFAC-PapersOnLine 52, no. 5: 492-497.
The paper designs a semi-active suspension control strategy for a supercar. The control strategy aims at providing sprung mass stability in terms of heave, pitch and roll dynamics. Supercars dynamics are very demanding in that the vehicle has to provide excellent stability during pilot-induced sprung mass movements (braking, acceleration and turning) and a reasonable road disturbance isolation. The proposed control system is based on four independent modified sky-hook controllers and a centralized high-level controller that schedules the parameter of the sky-hook algorithms taking into account the driver’s input. The paper implements the proposed algorithm on an instrumented supercar and validates the approach on a number of maneuvers.
Matteo Corno; Olga Galluppi; Giulio Panzani; Andrea Sinigaglia; Paolo Capuano; Jacopo Cecconi; Sergio M. Savaresi. Design and Validation of a Full Body Control Semi-Active Suspension Strategy for a Supercar. IFAC-PapersOnLine 2019, 52, 667 -672.
AMA StyleMatteo Corno, Olga Galluppi, Giulio Panzani, Andrea Sinigaglia, Paolo Capuano, Jacopo Cecconi, Sergio M. Savaresi. Design and Validation of a Full Body Control Semi-Active Suspension Strategy for a Supercar. IFAC-PapersOnLine. 2019; 52 (5):667-672.
Chicago/Turabian StyleMatteo Corno; Olga Galluppi; Giulio Panzani; Andrea Sinigaglia; Paolo Capuano; Jacopo Cecconi; Sergio M. Savaresi. 2019. "Design and Validation of a Full Body Control Semi-Active Suspension Strategy for a Supercar." IFAC-PapersOnLine 52, no. 5: 667-672.
Tire-road friction is the most important characteristic defining the planar dynamics of wheeled vehicles. It has consequences on the drivability, stability and tuning of the active vehicle dynamics control systems. This paper proposes two online friction estimation methods designed for the adaptation of vehicle dynamics control algorithms. The problem is framed as a classification problem where inertial measurements are used to discriminate between high and low friction regimes. The first method merges a recursive least-squares (RLS) algorithm with a heuristic bistable logic to classify the friction condition and promptly react to its changes. The second method runs a classification algorithm on the slip-acceleration characteristic. Both methods simultaneously account for the longitudinal and lateral dynamics and are tested on experimental data.
Donald Selmanaj; Matteo Corno; Sergio M. Savaresi. Friction State Classification Based on Vehicle Inertial Measurements. IFAC-PapersOnLine 2019, 52, 72 -77.
AMA StyleDonald Selmanaj, Matteo Corno, Sergio M. Savaresi. Friction State Classification Based on Vehicle Inertial Measurements. IFAC-PapersOnLine. 2019; 52 (5):72-77.
Chicago/Turabian StyleDonald Selmanaj; Matteo Corno; Sergio M. Savaresi. 2019. "Friction State Classification Based on Vehicle Inertial Measurements." IFAC-PapersOnLine 52, no. 5: 72-77.
This paper presents a self-learning strategy to control the deactivation of cylinders in large displacement automotive engines. Cylinder deactivation is a useful strategy to reduce emissions and improve fuel consumption without limiting the maximum engine power. A cylinder deactivation algorithm has to balance the target of reducing emissions with drivability concerns. Changing the cylinder configuration at the wrong time or too often negatively affects drivability and fuel efficiency. The paper proposes a self-learning algorithm inspired by the Markov Chain formalism. The proposed algorithm predicts the future fuel consumption and determines the best cylinder configuration based on that prediction. The prediction algorithm is learnt online using past inputs and thus adapts to the specific driver and conditions. Two versions of the algorithm are proposed that use two different inputs. The approaches are validated in simulation on an engine test-bed, showing that the self-learning algorithm yields fuel savings up to 10% without affecting drivability in the New European Driving Cycle.
Matteo Corno; Luca D'Avico; Stefano Marelli; Marco Galvani; Sergio M. Savaresi. Predictive Cylinder Deactivation Control for Large Displacement Automotive Engines. IEEE Transactions on Vehicular Technology 2019, 68, 9554 -9563.
AMA StyleMatteo Corno, Luca D'Avico, Stefano Marelli, Marco Galvani, Sergio M. Savaresi. Predictive Cylinder Deactivation Control for Large Displacement Automotive Engines. IEEE Transactions on Vehicular Technology. 2019; 68 (10):9554-9563.
Chicago/Turabian StyleMatteo Corno; Luca D'Avico; Stefano Marelli; Marco Galvani; Sergio M. Savaresi. 2019. "Predictive Cylinder Deactivation Control for Large Displacement Automotive Engines." IEEE Transactions on Vehicular Technology 68, no. 10: 9554-9563.